library(tidyverse)
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## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
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library(lubridate)
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## date
time_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# Create Keys
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
# Plot graph to a png outputfile
ppi <- 300
png("images/us_deaths.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()
## quartz_off_screen
## 2
US COVID-19 Deaths
library(plotly)
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## layout
US_deaths <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
#filtered by Country US and deaths
filter (Country_Region == "US")
p <- ggplot(data = US_deaths, aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
ggplotly(p)
library(gganimate)
library(transformr)
theme_set(theme_bw())
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
#plotting confirmed cases in China, South Korea, Japan, Italy, and the US
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
animate(p, end_pause = 15)
ppi <- 300
png("images/italy_confirmed.png", width=3*ppi, height=3*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
#filter country by Italy and the confirmed cases
filter (Country_Region == "Italy") %>%
ggplot(aes(x = Date, y = Confirmed)) +
geom_point() +
geom_line() +
ggtitle("Italy COVID-19 Confirmed")
dev.off()
## quartz_off_screen
## 2
Italy COVID-19 Deaths
z <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("US", "Brazil", "United Kingdom", "Italy", "Mexico", "France", "Spain", "India", "Iran", "Peru")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")
ggplotly(z)
z <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("US", "Brazil", "United Kingdom", "Italy", "Mexico", "France", "Spain", "India", "Iran", "Peru"))
p <- ggplot(z, aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")+
geom_point(aes(group=seq_along(Date)))+
transition_reveal(Date)
animate(p, end_pause = 15)